Mixed treatment comparisons using aggregate and individual participant level data.

Mixed treatment comparisons (MTC) extend the traditional pair-wise meta-analytic framework to synthesize information on more than two interventions. Although most MTCs use aggregate data (AD), a proportion of the evidence base might be available at the individual level (IPD). We develop a series of novel Bayesian statistical MTC models to allow for the simultaneous synthesis of IPD and AD, potentially incorporating study and individual level covariates. The effectiveness of different interventions to increase the provision of functioning smoke alarms in households with children was used as a motivating dataset. This included 20 studies (11 AD and 9 IPD), including 11 500 participants. Incorporating the IPD into the network allowed the inclusion of information on subject level covariates, which produced markedly more accurate treatment-covariate interaction estimates than an analysis solely on the AD from all studies. Including evidence at the IPD level in the MTC is desirable when exploring participant level covariates; even when IPD is available only for a fraction of the studies. Such modelling may not only reduce inconsistencies within networks of trials but also assist the estimation of intervention subgroup effects to guide more individualised treatment decisions.